In Computer Graphics and Computer Vision, shape co-segmentation and shape-matching are fundamental tasks with diverse applications, from statistical shape analysis to human-robot interaction. These problems respectively target establishing segment-to-segment and point-to-point correspondences between shapes, which are crucial task for numerous practical scenarios. Notably, co-segmentation can aid in point-wise correspondence estimation in shape-matching pipelines like the functional maps framework. Our paper introduces an innovative shape segmentation pipeline which provides coherent segmentation for shapes within the same class. Through comprehensive evaluation on a diverse test set comprising shapes from various datasets and classes, we demonstrate the coherence of our segmentation approach. Moreover, our method significantly improves accuracy in shape matching scenarios, as evidenced by comparisons with the original functional maps approach. Importantly, these enhancements come with minimal computational overhead. Our work not only introduces a novel coherent segmentation method and a valuable tool for improving correspondence accuracy within functional maps, but also contributes to the theoretical foundations of this impactful field, inspiring further research.

Mancinelli, C., Melzi, S. (2023). Spectral-based Segmentation for Functional Shape-matching. In Eurographics Italian Chapter Proceedings - Smart Tools and Applications in Graphics, STAG (pp.47-58). Eurographics Association [10.2312/stag.20231294].

Spectral-based Segmentation for Functional Shape-matching

Melzi, S
2023

Abstract

In Computer Graphics and Computer Vision, shape co-segmentation and shape-matching are fundamental tasks with diverse applications, from statistical shape analysis to human-robot interaction. These problems respectively target establishing segment-to-segment and point-to-point correspondences between shapes, which are crucial task for numerous practical scenarios. Notably, co-segmentation can aid in point-wise correspondence estimation in shape-matching pipelines like the functional maps framework. Our paper introduces an innovative shape segmentation pipeline which provides coherent segmentation for shapes within the same class. Through comprehensive evaluation on a diverse test set comprising shapes from various datasets and classes, we demonstrate the coherence of our segmentation approach. Moreover, our method significantly improves accuracy in shape matching scenarios, as evidenced by comparisons with the original functional maps approach. Importantly, these enhancements come with minimal computational overhead. Our work not only introduces a novel coherent segmentation method and a valuable tool for improving correspondence accuracy within functional maps, but also contributes to the theoretical foundations of this impactful field, inspiring further research.
slide + paper
Spectral shape analysis; Shape segmentation, Shape matching; Functional maps
English
10th Eurographics Italian Chapter Conference on Smart Tools and Applications in Graphics, STAG 2023 - 16 November 2023 through 17 November 2023
2023
Fellner, D; Manfredi, G; Caggianese, G; Capece , N; Ugo, E; Banterle, F; Lupinetti, K
Eurographics Italian Chapter Proceedings - Smart Tools and Applications in Graphics, STAG
9783038682356
2023
47
58
open
Mancinelli, C., Melzi, S. (2023). Spectral-based Segmentation for Functional Shape-matching. In Eurographics Italian Chapter Proceedings - Smart Tools and Applications in Graphics, STAG (pp.47-58). Eurographics Association [10.2312/stag.20231294].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/463859
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